{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a notebook that you can use to experiment with the jupytext package.\n",
    "\n",
    "Proposed experimentations are\n",
    "1. Open this Jupyter notebook (extension .ipynb) in Jupyter\n",
    "2. Open the corresponding '.py' file. That is, copy the url for the notebook, that looks like\n",
    "```\n",
    "https://hub.mybinder.org/user/mwouts-jupytext-g7315gce/notebooks/demo/Sample%20notebook%20with%20python%20representation.ipynb\n",
    "```\n",
    "replace `/notebooks/` with `/edit/`, and change the extension from `ipynb` to `py`, to get an url like\n",
    "```\n",
    "https://hub.mybinder.org/user/mwouts-jupytext-g7315gce/edit/demo/Sample%20notebook%20with%20python%20representation.py\n",
    "```\n",
    "3. Modify the notebook in Jupyter. Save. Refresh the python file in the editor, and observe the changes.\n",
    "4. Now, modify the python file, save, and refresh the Jupyter notebook. Observe how inputs were updated, outputs preserved when possible, and kernel was left unchanged.\n",
    "5. Browse the github repo using Jupyter in binder, and open arbitrary python files as notebooks. Run them when applicable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'About as simple as it gets, folks')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = 1 + np.sin(2*np.pi*t)\n",
    "plt.plot(t, s)\n",
    "\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('voltage (mV)')\n",
    "plt.title('About as simple as it gets, folks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
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